Some History

Ask 10 academics and get 10 different answers, but at it’s most basic we’re talking about a virtual representation of a physical object. The term seems to have emerged around 2005, but it’s not hard to trace it back substantially further to, for example, Gelernter (1993)’s Mirror World, or the Day Software Puts the Universe in a Shoebox…. In a built environment context, as the two images on the left show we might even trace it back to the 1980s — top is a model of an English village, and below that is an output from a SOM Architect generated model for 9 US cities — and attempts to understand the relationship between different forms and masses in the real world. On the right is an early attempt to superimpose other contextual information — pollution in this case — on to a 3D surface.

Models and Abstractions

A model is an abstraction or simplification of the real thing, where the model-builder throws away most of the real thing leaving only its essence.

Models reflect

Twins interact

So we often think of models as a way to reflect some real-world system in a mathematical or computational context; however the twin isn’t intended just to represent the real world, it is built to interact with it in some way. For physical processes, such as electrical or mechanical systems the difference between a model and a twin can be fairly minimal, and the model itself may be reasonably close to the real thing. The twin will be tightly coupled. To put it another way, with the right tooling, the flow of information between model and world can be bidirectional and relatively simple.

But for social and economic processes – such as cities – the representations of any underlying reality is far more abstract and the interactions are far more complex. In the latter case, the flow of information is also complex: even if we did manage to model the entire city, the transfer of information from twin to reality is no longer automated and may even be non-digital such as, for example, the way that model outputs can change our underlying understanding of the problem that we are trying to tackle!

So the attempt to make, for example, an urban digital twin is deeply problematic – which is not to say that it’s not worth doing – because of the interaction element.

Urban Analytics		High Frequency
Urban Theory¹		Low Frequency

The instrumentation of infrastructures and mobility platforms, and the spread of computation into every thing around us, also presents us with the challenge of frequency. For high frequency, you can think of ‘smart’ systems, whether they are the ‘smart trees’ at this year’s Chelsea Garden Show or the ‘smart cities’ that have been a developmental priority in many countries. For most models and twins it’s relatively straightforward to peer a short way into the future.

That’s why, in the high-frequency world of urban analytics it seems plausible that we can use the twin to influence the real thing. Real-time data about the Tube system doesn’t just mirror what’s happening in the Tube, it can be used to shape and change the deployment of people and infrastructures.

But when we try to use high frequency data to forecast what cities might look like in ten, twenty, or thirty years we run into huge issues: indeed, “our current predictions for many social processes are at present not better than two-to-three-day weather forecasting.” So there is a real danger in attempting to use high-frequency data or urban analytics techniques on low frequency problems, and a big part of that is the absence of guardrails when you don’t begin with any kind of theory about the system you are modelling.

Shallow copy		Copy by reference
Deep copy		Copy by value

If we pull the ideas from the preceding slides together, there’s a nice analogy to how programmers distinguish between shallow and deep copies of objects and data structures. They mean this in a very particular way about copying references (pointers to the real data) and copying values (the data itself).

Copying by reference is fast and efficient – especially when you’re dealing with large volumes of data – but changes to the reference update the original. Copying by value replicates the object itself in a new memory space where changes to the copy don’t affect the original. So that’s slower, less efficient, but can also be safer. When you copy by value on large data sets you often have to think rather carefully about what data you actually need.

So at the risk of stretching this analogy to breaking point, all of this throws up a deeper meaning as well: perhaps our digital twins copy normative values as well? Like the model-builder, in building a digital twin we make choices about what to keep and what to throw away. Clearly, we also seek to interact with the real-world, but we need to decide if our aim is high- or low-frequency interaction.

If the interaction is low frequency then things get harder: is the flow of information direct, or is there a human-in-the-loop which necessarily going to profoundly alter the nature of the interaction. What we’re getting to here is that maybe there is no such thing as a shallow copy of a city?

A Federation of Computers

A Federation of Twins

\(\ldots\) in social systems, we can define many types of twins that reflect a multitude of theoretical perspectives, each of which reflect different features of the real system

The Human in the Loop

Efficiency & Resilience

Highly efficient system \(\approx\) brittle
Highly redundant system \(\approx\) resilient

Thank you!

Arribas-Bel, D., and J. Reades. 2018. “Geography and Computers: Past, Present, and Future.” Geography Compass 12 (e12403). https://doi.org/10.1111/gec3.12403.

Batty, M. 1990. “Invisible Cities.” Environment and Planning B 17 (2):127–30.

———. 2013. “Big Data, Smart Cities and City Planning.” Dialogues in Human Geography 3 (3). SAGE Publications:274–79.

———. 2018. “Digital Twins.” Environment and Planning B: Urban Analytics and City Science. Sage Publications Sage UK: London, England.

———. 2019. “A Map Is Not the Territory, or Is It?” Environment and Planning B: Urban Analytics and City Science. SAGE Publications Sage UK: London, England.

———. 2021. “Multiple Models.” Environment and Planning B: Urban Analytics and City Science. SAGE Publications Sage UK: London, England.

———. 2024. “Digital Twins in City Planning.” Nature Computational Science 4 (3). Nature Publishing Group US New York:192–99.

Batty, M., and R. Milton. 2023. “Building a Digital Twin for British Cities.” arXiv Preprint arXiv:2312.03674.

Gelernter, David. 1993. Mirror Worlds: Or the Day Software Puts the Universe in a Shoebox... How It Will Happen and What It Will Mean. Oxford University Press.

Reades, J., F. Calabrese, A. Sevtsuk, and C. Ratti. 2007. “Cellular census: Explorations in urban data collection.” IEEE Pervasive Computing 6 (3):30–38.

Reades, J., J. De Souza, and P. Hubbard. 2019. “Understanding Urban Gentrification Through Machine Learning.” Urban Studies 56 (5):922–42.